Deep learning (DL) is a branch of machine learning and artificial neural network based on a set of algorithms that attempt to model high level abstractions in data by using a deep graph with multiple processing layers. A typical DL architecture can include many layers of neurons and millions of parameters. These parameters can be trained from large amount of data on fast GPU-equipped computers, guided by novel training techniques that can work with many layers, such as rectified linear units (ReLU), dropout, data augmentation, and stochastic gradient descent (SGD).
Among the existing DL architectures, convolutional neural network (CNN) is one of the most popular DL architectures. Although the idea behind CNN has been known for more than 20 years, the true power of CNN has only been recognized after the recent development of the deep learning theory. To date, CNN has achieved numerous successes in many artificial intelligence and machine learning applications, such as face recognition, image classification, image caption generation, visual question answering, and automatic driving cars.
Face detection is an important process in many face recognition applications. A large number of face detection techniques can easily detect near frontal faces.
In the face recognition as such, if a facial image is inputted, a feature recognition network extracts features from the inputted facial image, and a face is recognized by using the extracted features.
However, even if new facial images of a same person are continuously added through applications which use the face recognition such as an SNS and a photo management system, the new facial images may not always be added due to a limited capacity of a database, that is, a storage device. Also, in that case, when searching for a specific facial image, its searching time is increased.
Conventionally, in order to eliminate this problem, when the number of database records of a single person exceeds the number allocated to the single person, the oldest record is deleted or the facial image with the lowest similarity among the images of the single person is deleted. However, there still remains an unsolved problem that the deleted facial images may not be the ones that should have been deleted in terms of the face recognition.
Also, conventionally, a representative face is selected to shorten the searching time for the facial image of a specific person in a database.
However, when selecting the representative face, if the database stores N people and M facial images for each of the N people, M×N matching is required for one query.
In addition, a scheme for selecting a representative face among M faces is needed to reduce the searching time, and as one example of the scheme, a representative facial image is selected by choosing a facial image having the highest similarity among the M facial images or using an average feature of the M facial images. However, in terms of face recognition performance, it is difficult to guarantee that the selected representative facial image is the most optimal facial image.
Accordingly, the inventors of the present disclosure propose a method and a device for efficiently managing a smart database where new facial images are continuously added.